Covariance estimation

import pandas as pd
pd.options.plotting.backend = "plotly"

# Load historic price data of 20 stocks
prices=pd.read_csv("data/stock_prices.csv", header=0, index_col=0, parse_dates=True) 

# Compute the historic returns
returns = prices.pct_change().dropna(axis=0, how="all")[["GOOG", "AAPL", "FB"]]

from cvx.covariance.combination import from_ewmas

# Pairs of halflife volatility vs halflife covariances
pairs = [(10, 10), (21, 21), (21, 63)]

combinations = from_ewmas(
    returns,
    pairs,
    clip_at=4.2,
    mean=True
)

weights = pd.DataFrame({result.time : result.weights for result in combinations.solve(window=10)}).transpose()
weights

	10-10	21-21	21-63
2017-03-15	1.000000e+00	2.408422e-09	2.495247e-09
2017-03-16	1.000000e+00	6.696406e-09	8.048516e-09
2017-03-17	1.000000e+00	5.090497e-09	6.028853e-09
2017-03-20	1.000000e+00	7.147203e-09	1.184262e-08
2017-03-21	1.393968e-08	1.000000e+00	3.310787e-08
...	...	...	...
2018-04-05	1.047505e-08	1.000000e+00	2.446955e-08
2018-04-06	7.062781e-09	1.000000e+00	1.658520e-08
2018-04-09	5.972326e-09	1.000000e+00	2.305724e-08
2018-04-10	2.909872e-07	9.999997e-01	2.763267e-08
2018-04-11	6.118023e-09	9.999999e-01	6.318126e-08

271 rows × 3 columns

weights.plot()